109 lines
4.2 KiB
Markdown
109 lines
4.2 KiB
Markdown
# Mobility and Smart Cities
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## GILA - Greedy Incremental Louage’s Algorithm
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### Presentation
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Louage is a transportation system in Tunisia to overcome the lack of public transportation. This system is spread across all the Tunisian territory and is used by a lot of people. In this system their are two types of trips : regional and cross-regional.
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This transportation system is very cheap and have a very high occupation level (97%).
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Drivers propose their itineraries and stops with a time window of departure and arrival and a price per seat.
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Passengers can ask to book a trip from a location to a destination with a desire departure and an arrival time window.
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Operators assigned passengers to a trip if the trip is compatible with their time window and if there is enough seats available. First come first served (FIFO).
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Other criteria could also be used like pricing, number of stops, number of changes require to achieve the trip etc.
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### Problem
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The Louage probleme is a multi-constraints and multi-objectives problem.
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Objectives :
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- Maximize the number of passengers per vehicle (High occupation rate) to maximize the profit of the driver
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- Maximize the number of satisfied requests (e.g. maximize the number of clients)
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- Respect first in first out (FIFO) rule
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- Minimize the waiting time of the passengers
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- Minimize the overall sum of running itineraries duration
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- As soon as a louage is full, it should leave
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Constraints :
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- Coherence of time windows and travel time with normalization
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- Louage’ s lines constraints
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- Itinerary constraints
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- Stop position depencies respected between request and organization (set of journeys)
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- Respect the order of status and conditions on state transition for each journey
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- Capacity of the vehicle
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- Price
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How can we assigned travelers to their corresponding while respecting the constraints and maximizing the objectives ?
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### Conception
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To solve this problem the GILA algorithm uses a greedy approach. We makes the best decision at each step to reach a local optimum.
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#### Inputs
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We have two types of input in different lists : demands and offers.
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##### Demands
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Each client create a demand with the following information :
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- Itinerary / Path (origin, destination)
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- Contains all the stops of the itinerary
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- Time window of departure (hd_minus, hd_plus)
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- Time window of arrival (ha_minus, ha_plus)
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- Booked placed (NP)
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- Bill
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- State (initialized, validated, paired, contractualized, running, realized, non realized and archived), this is used to keep track of the state of the demand during the algorithm.
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##### Offers
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Each driver realized a trip with the following information :
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- Itinerary / Path (origin, destination)
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- Contains all the stops of the itinerary
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- Time window of departure (hd_minus, hd_plus)
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- Time window of arrivals (ha_minus, ha_plus)
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- Number of available seats (PlacesL)
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- The driver
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- The vehicle
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- Status
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#### Output
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As a result, the algorithm returns an organization of journeys composed of associated the demands and offers.
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Each journey is composed of :
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- an itinerary
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- an offer
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- a list of demands
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- a state
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- a maximal number of seats occupied by passengers at any step of the itinerary
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- the number of available seats at any step of the itinerary
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#### Normalization
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When clients are affected to a trip, their time window of departure and arrival are updated to take into account the traveling time and the time window of departure and arrival of the louage.
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We have an offer leaving between 8h and 8h30 for a trip that takes 30min and arriving between 8h to 9h30.
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Normalization will shift the arrival time window of the offer to 8h30 and 9h30.
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Trips must also be coherent:
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- each status of journey must be in the defined order
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- it's impossible to drop someone before picking him up so pickup location have to be before dropoff location in the itinerary
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### Examples
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#### Status
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#### Pairing
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#### Normalization
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